Dataset ID:	720
Last updated:	2010-04-24
creador del conjunto de datos:	Jörg Bendix
Contacto:	Maik Dobbermann
cobertura temporal:	1999-01-01 18:25:00 - 2007-12-31 18:25:00 - time zone: -5
cobertura geografica:	Spatial plot (as in Erdkunde article)
Resumen:	--- Resumen:
Informaciones adicionales:	Publication: http://137.248.191.82/publications.do... Informaciones adicionales: Publication: http://137.248.191.82/publications.do?citid=718
Palabras claves:	\| temperature \| raster \| TMin \| average monthly \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	month minimal air temperature
Entity type:	spatialRaster (511 x 479)
Atributos:	average minimum Temperature in January (TMinAverageJan) [Degrees Celsius]

Entity name:	month minimal air temperature
Entity type:	spatialRaster (511 x 479)
Atributos:	average minimum Temperature in February (TMinAverageFeb) [Degrees Celsius]

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DEM 90m (SRTM)

Dataset ID:	900
Last updated:	2012-03-02
creador del conjunto de datos:	Thomas Lotz
Contacto:	Thomas Lotz
cobertura temporal:	2000-02-01 00:00:00 - 2000-02-29 23:59:00 - time zone: -5
cobertura geografica:	South Ecuador
Resumen:	This might not be the latest version (and highest ... Resumen: This might not be the latest version (and highest quality) of SRTM data. Use http://srtm.csi.cgiar.org/ to download the latest version. Information about processing are rare.
Informaciones adicionales:	--- Informaciones adicionales:
Palabras claves:	\| raster \| DEM \| base data \| elevation \| SRTM \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	DEM 90m
Entity type:	spatialRaster (3600 x 3600)
Atributos:	elevation (El) [Meter]

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Mode of vegetation heigh in the vertical density function

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vea metadata

Dataset ID:	1444
Last updated:	2016-02-22
creador del conjunto de datos:	Brenner Silva
Contacto:	Brenner Silva
cobertura temporal:	2012-03-07 00:00:00 - 2012-11-19 00:00:00 - time zone: -5
cobertura geografica:	San francisco basin
Resumen:	This dataset represents the statistical mode of ve... Resumen: This dataset represents the statistical mode of vegetation heigh in the vertical density function. This is a second level product of the LiDAR survey.
Informaciones adicionales:	The usage of the LiDAR data is restricted to the R... Informaciones adicionales: The usage of the LiDAR data is restricted to the RU816 members. The usage for publications has to be agreed in advance with Prof. J. Bendix. In addition please check the PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Palabras claves:	\| raster \| canopy \|
Derechos intelectuales:	PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)

Entity name:	sfmetrics_mdh
Entity info:	Mode of vegetation heigh in the vertical density function
Entity type:	otherEntity

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CHM 1m (LiDAR) San Francisco basin

Dataset ID:	1442
Last updated:	2021-08-12
creador del conjunto de datos:	Brenner Silva Joerg Zeilinger Jörg Bendix
Contacto:	Brenner Silva
cobertura temporal:	2012-03-07 00:00:00 - 2012-11-19 00:00:00 - time zone: -5
cobertura geografica:	San francisco basin
Resumen:	The Canopy Height Model(CHM) is the calculated dif... Resumen: The Canopy Height Model(CHM) is the calculated difference between DSM and DTM, the first products of the LiDAR survey. The second entity is the canopy height model in 10 m resolution derived from the LiDAR survey. The helicopter-based survey of the Rio San Francisco catchment has been conducted in the periods of March 7-17, 2012 and November 12-19, 2012. Coordinate System UTM, Datum WGS84, Zone 17S, and Geoid EGM96 - for orthometric height - are used. The horizontal accuracy is within 5cm (based on control landmark of the IGM geodetic network). Quality assurance is done by visually inspection and correction of classification and ghost reflections. Additional quality control is done for completeness of the point cloud density and for models creation, which assured internal consistency of terrain representation (removal of artifacts due to misclassification) and seamless integration between tiles. Total data extent, considering all tiles is W: 704190.00, S: 9552910.00, N: 718190.00, E:9564110.00. The DTM has the spatial resolution of 1m, which is confirmed based on independent GPS control points measured on plane areas. The LiDAR survey has been carried out under the working group of Prof. J. Bendix. Operational support and quality check: Jörg Zeilinger, Brenner Silva, Thomaz Lotz
Informaciones adicionales:	The usage of the LiDAR DSM/DTM is restricted to th... Informaciones adicionales: The usage of the LiDAR DSM/DTM is restricted to the RU816 members. The usage for publications has to be agreed in advance with Prof. J. Bendix. In addition please check the PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Palabras claves:	\| height \| raster \| canopy \|
Derechos intelectuales:	This dataset is released under the CC-BY license. https://creativecommons.org/licenses/by/3.0/de/deed.en Please use the citation string (Cite as:) in the metadata to fulfill the attribution part.

Entity name:	dsm-dtm_difference_1m
Entity info:	CHM 1m (derived from LiDAR survey)
Entity type:	otherEntity

Entity name:	sfmetrics_ch
Entity info:	CHM 10m (derived from LiDAR survey
Entity type:	otherEntity

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Average monthly 2m maximum (TMax) air temperatures in the study area

Dataset ID:	690
Last updated:	2010-04-24
creador del conjunto de datos:	Jörg Bendix
Contacto:	Maik Dobbermann
cobertura temporal:	1999-01-01 19:32:00 - 2007-12-31 19:33:00 - time zone: -5
cobertura geografica:	Spatial plot (as in Erdkunde article)
Resumen:	--- Resumen:
Informaciones adicionales:	--- Informaciones adicionales:
Palabras claves:	\| temperature \| raster \| average monthly \| TMax \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	month max mean temperatur
Entity type:	spatialRaster (511 x 479)
Atributos:	average maximum Temperature in January (TMaxAverageJan) [Degrees Celsius]

Entity name:	month max mean temperatur
Entity type:	spatialRaster (511 x 479)
Atributos:	average maximum Temperature in February (TMaxAverageFeb) [Degrees Celsius]

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Discrete Vertical Distribution Ratio

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vea metadata

Dataset ID:	1443
Last updated:	2016-02-22
creador del conjunto de datos:	Brenner Silva
Contacto:	Brenner Silva
cobertura temporal:	2012-03-07 00:00:00 - 2012-11-19 00:00:00 - time zone: -5
cobertura geografica:	San francisco basin
Resumen:	This dataset represents the vertical distribution ... Resumen: This dataset represents the vertical distribution of intercepted canopy elements (biomass). This is a second level product from the LiDAR survey.
Informaciones adicionales:	The usage of the LiDAR DSM/DTM is restricted to th... Informaciones adicionales: The usage of the LiDAR DSM/DTM is restricted to the RU816 members. The usage for publications has to be agreed in advance with Prof. J. Bendix. In addition please check the PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Palabras claves:	\| raster \| canopy \| aboveground biomass \|
Derechos intelectuales:	PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)

Entity name:	sfmetrics_dvdr
Entity info:	Vertical distribution of intercepted canopy elements (biomass)
Entity type:	otherEntity

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Map of Reference Soil Groups (WRB)

Dataset ID:	897
Last updated:	2010-09-13
creador del conjunto de datos:	Mareike Ließ
Contacto:	Mareike Ließ
cobertura temporal:	2001-05-13 11:32:00 - 2001-05-13 11:32:00 - time zone: -5
cobertura geografica:	ECSF DEM 10m
Resumen:	Soil-landscape modelling is based on understanding... Resumen: Soil-landscape modelling is based on understanding the spatial distribution patterns of soil characteristics. A model relating the soil?s properties to its position within the landscape is used to predict soil properties in other similar landscape positions. To develop soil landscape models, the interaction of geographic information technology, advanced statistics and soil science is needed. The focus of this work is to predict the distribution of the different soil types in a tropical mountain forest area in southern Ecuador from relief and hydrological parameters using a classification tree model (CART) for soil regionalisation. Soils were sampled along transects from ridges towards side valley creeks using a sampling design with 24 relief units. Major soil types of the research area are Histosols associated with Stagnosols, Cambisols and Regosols. Umbrisols and Leptosols are present to a lesser degree. Stagnosols gain importance with increasing altitude and with decreasing slope angle. Umbrisols are to be found only on slopes <30°. Cambisols occurrence might be related to landslides. The CART model was established by a data set of 315 auger sampling points. Bedrock and relief curvature had no influence on model development. Applying the CART model to the research area, Histosols and Stagnosols were identified as dominant soil types. Model prediction left out Cambisols and overestimated Umbrisols, but showed a realistic prediction for Histosols, Stagnosols and Leptosols.
Informaciones adicionales:	Number codes: 2= Histosol, 3=Leptosol,4= Regosol,... Informaciones adicionales: Number codes: 2= Histosol, 3=Leptosol,4= Regosol, 5= Stagnosol,6= Umbrisol
Palabras claves:	\| soil characteristics \| soil classification \| output \| raster \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	soil type distribution
Entity type:	spatialRaster (598 x 507)
Atributos:	Soil type after "world reference base" (Soil_typeWRB) (sin unidad)

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DEM 10m (triangulated from aerial photo - b/w)

Dataset ID:	901
Last updated:	2013-03-01
creador del conjunto de datos:	Lars Ungerechts
Contacto:	Thomas Lotz
cobertura temporal:	1997-01-01 00:00:00 - 1997-01-01 00:00:00 - time zone: -5
cobertura geografica:	Loja region
Resumen:	The DEM is generated from stereo aerial photos by ... Resumen: The DEM is generated from stereo aerial photos by aero-triangulation. The processing was done by and is described in: Jordan, E.; Ungerechts, L.; Cáceres, B.; Penafiel, A. and Francou, B. (2005): Estimation by photogrammetry of the glacier recession on the Cotopaxi Volcano (Ecuador) between 1956 and 1997. In: Hydrological Sciences 50, 949-961. Information about further processing are rare. Information about the quality are also rare. Triangulation artefacts in shadowed areas of the photos are known.
Informaciones adicionales:	--- Informaciones adicionales:
Palabras claves:	\| raster \| DEM \| base data \| elevation \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	DEM 10m
Entity type:	spatialRaster (6257 x 6625)
Atributos:	elevation (El) [Meter]

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Landcover classification Ecuador (10 classes)

Dataset ID:	704
Last updated:	2009-10-23
creador del conjunto de datos:	Dietrich Göttlicher
Contacto:	Dietrich Göttlicher
cobertura temporal:	2001-11-03 16:57:00 - 2001-11-03 16:57:00 - time zone: -5
cobertura geografica:	Landsat subset south ecuador (podocarpus)
Resumen:	--- Resumen:
Informaciones adicionales:	Creating a classification with landsat ETM+. Prepr... Informaciones adicionales: Creating a classification with landsat ETM+. Preprocessing includes: Reprojection: Idrisi Import / Reproject, Projection: UTM 17 South (30 m pixel size) Subsetting: Idrisi Window, min x : 684990, max x : 758010, min y: 9493010, max y: 9601610 Geometric correction: ERDAS Imagine 8.6, Geometric model: Landsat Reference: Landsat Geocover datasets (1986, 2000, orthorectified), Digital elevation model: SRTM data, Ground Control Points: 152, Control Point Error : (X) 12.68 (Y) 13.42 (Total) 18.46 (<1 pixel), Subsetting: min x: 684990, max x: 757710,min y: 9493010, max y: 9599390 Atmospheric correction: Idrisi Atmosc (Cost model), Year: 2001, Month: 11, Day: 03, GMT: 15.00, Sun elevation: 61.8568494 Band Center (Cm) DN haze Offset Gain 1 0.485 27 -0.73807 0.11807 2 0.56 19 -0.76098 0.12098 3 0.66 12 -0.59425 0.09425 4 0.83 6 -0.60693 0.09693 5 1.65 3 -0.11912 0.01912 7 2.215 5 -0.04165 0.00665 Topographic correction: Statistical-empirical approach Idrisi Hillshade Digital elevation model: SRTM data Sun elevation: 61.8568494 Sun azimuth: 114.5874861 CLASSIFICATION: Step 1) Maximum likelihood classifier with 19 different training sites Step 2) Post-classification merging to 15 and 10 classes
Palabras claves:	\| raster \| classification \| land cover \| landsat \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	landcover classification ecuador
Entity type:	spatialRaster (2424 x 3546)
Atributos:	land use (Land_use) (sin unidad)

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Landcover soft classification Ecuador (15 classes)

Dataset ID:	706
Last updated:	2009-10-26
creador del conjunto de datos:	Dietrich Göttlicher
Contacto:	Dietrich Göttlicher
cobertura temporal:	2001-11-03 18:33:00 - 2001-11-03 18:33:00 - time zone: -5
cobertura geografica:	Landsat subset south ecuador (podocarpus) for soft classification
Resumen:	--- Resumen:
Informaciones adicionales:	Creating a classification with landsat ETM+. Prepr... Informaciones adicionales: Creating a classification with landsat ETM+. Preprocessing includes: Reprojection: Idrisi Import / Reproject, Projection: UTM 17 South (30 m pixel size) Subsetting: Idrisi Window, min x : 684990, max x : 758010, min y: 9493010, max y: 9601610 Geometric correction: ERDAS Imagine 8.6, Geometric model: Landsat Reference: Landsat Geocover datasets (1986, 2000, orthorectified), Digital elevation model: SRTM data, Ground Control Points: 152, Control Point Error : (X) 12.68 (Y) 13.42 (Total) 18.46 (<1 pixel), Subsetting: min x: 684990, max x: 757710,min y: 9493010, max y: 9599390 Atmospheric correction: Idrisi Atmosc (Cost model), Year: 2001, Month: 11, Day: 03, GMT: 15.00, Sun elevation: 61.8568494 Band Center (Cm) DN haze Offset Gain 1 0.485 27 -0.73807 0.11807 2 0.56 19 -0.76098 0.12098 3 0.66 12 -0.59425 0.09425 4 0.83 6 -0.60693 0.09693 5 1.65 3 -0.11912 0.01912 7 2.215 5 -0.04165 0.00665 Topographic correction: Statistical-empirical approach Idrisi Hillshade Digital elevation model: SRTM data Sun elevation: 61.8568494 Sun azimuth: 114.5874861 CLASSIFICATION: Step 1) Maximum likelihood classifier with 19 different training sites Step 2) Post-classification merging to 15 and 10 classes class 1 = Kammlage 1900-2100 class 2 = Subparamo > 2700 class 3 = Kammlagen > 2500 (gestört) class 4 = Hanglage > 2600 class 5 = Weide class 6 = vegetationslos (Strasse) class 7 = ehemalige Weide (Sukzession) class 8 = Hanglage 2200-2600 class 9 = Hanglage 1800-2100 class 10 = Kammlage 2100-2300 class 11 = Adlerfarn (Sukzession) class 12 = Kammlage > 2300 class 13 = Weide mit Busch und Waldresten class 14 = Rutschung class 15 = Wasser
Palabras claves:	\| land use \| raster \| classification \| landsat \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	likelihood class 1 (location on a ridge 1900-2100 masl)
Entity info:	one of 15 classes
Entity type:	spatialRaster (449 x 450)
Atributos:	land use (Land_use) (sin unidad)

Entity name:	likelihood class 2 (Subparamo > 2700 masl)
Entity info:	one of 15 classes
Entity type:	spatialRaster (449 x 450)
Atributos:	land use (Land_use) (sin unidad)

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Landcover classification Ecuador (15 classes)

Dataset ID:	705
Last updated:	2009-10-23
creador del conjunto de datos:	Dietrich Göttlicher
Contacto:	Dietrich Göttlicher
cobertura temporal:	2001-11-03 17:34:00 - 2001-11-03 17:35:00 - time zone: -5
cobertura geografica:	Landsat subset south ecuador (podocarpus)
Resumen:	--- Resumen:
Informaciones adicionales:	Creating a classification with landsat ETM+. Prepr... Informaciones adicionales: Creating a classification with landsat ETM+. Preprocessing includes: Reprojection: Idrisi Import / Reproject, Projection: UTM 17 South (30 m pixel size) Subsetting: Idrisi Window, min x : 684990, max x : 758010, min y: 9493010, max y: 9601610 Geometric correction: ERDAS Imagine 8.6, Geometric model: Landsat Reference: Landsat Geocover datasets (1986, 2000, orthorectified), Digital elevation model: SRTM data, Ground Control Points: 152, Control Point Error : (X) 12.68 (Y) 13.42 (Total) 18.46 (<1 pixel), Subsetting: min x: 684990, max x: 757710,min y: 9493010, max y: 9599390 Atmospheric correction: Idrisi Atmosc (Cost model), Year: 2001, Month: 11, Day: 03, GMT: 15.00, Sun elevation: 61.8568494 Band Center (Cm) DN haze Offset Gain 1 0.485 27 -0.73807 0.11807 2 0.56 19 -0.76098 0.12098 3 0.66 12 -0.59425 0.09425 4 0.83 6 -0.60693 0.09693 5 1.65 3 -0.11912 0.01912 7 2.215 5 -0.04165 0.00665 Topographic correction: Statistical-empirical approach Idrisi Hillshade Digital elevation model: SRTM data Sun elevation: 61.8568494 Sun azimuth: 114.5874861 CLASSIFICATION: Step 1) Maximum likelihood classifier with 19 different training sites Step 2) Post-classification merging to 15 and 10 classes
Palabras claves:	\| land use \| raster \| classification \| landsat \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	landsat classification ecuador 15 classes
Entity type:	spatialRaster (2424 x 3546)
Atributos:	land use (Land_use) (sin unidad)

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Average monthly 2m mean (TMean) air temperatures in the study area

Dataset ID:	730
Last updated:	2010-04-24
creador del conjunto de datos:	Jörg Bendix
Contacto:	Maik Dobbermann
cobertura temporal:	1999-01-01 18:25:00 - 2007-12-31 18:25:00 - time zone: -5
cobertura geografica:	Spatial plot (as in Erdkunde article)
Resumen:	Publication: http://137.248.191.82/publications.do... Resumen: Publication: http://137.248.191.82/publications.do?citid=718
Informaciones adicionales:	--- Informaciones adicionales:
Palabras claves:	\| air temperature \| raster \| average monthly \| TMean \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	average monthly mean temperature
Entity type:	spatialRaster (511 x 479)
Atributos:	average mean temperature in January (TMeanAverageJan) [Degrees Celsius]

Entity name:	average monthly mean temperature
Entity type:	spatialRaster (511 x 479)
Atributos:	average mean temperature in February (TMeanAverageFeb) [Degrees Celsius]

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DEM 1m (triangulated from aerial photo - color)

Dataset ID:	899
Last updated:	2013-03-01
creador del conjunto de datos:	Thomas Lotz
Contacto:	Thomas Lotz
cobertura temporal:	2001-01-01 00:00:00 - 2001-01-01 00:00:00 - time zone: -5
cobertura geografica:	ECSF DEM 1m
Resumen:	The DEM is generated from stereo aerial photos by ... Resumen: The DEM is generated from stereo aerial photos by aero-triangulation. The processing was done by and is described in: Jordan, E.; Ungerechts, L.; Cáceres, B.; Penafiel, A. and Francou, B. (2005): Estimation by photogrammetry of the glacier recession on the Cotopaxi Volcano (Ecuador) between 1956 and 1997. In: Hydrological Sciences 50, 949-961. Information about further processing are rare. Information about the quality are also rare. Triangulation artefacts in shadowed areas of the picture are known.
Informaciones adicionales:	--- Informaciones adicionales:
Palabras claves:	\| raster \| DEM \| base data \| elevation \|
Derechos intelectuales:	FOR816 data user agreement

Entity name:	DEM 1m
Entity type:	spatialRaster (5789 x 4876)
Atributos:	elevation (El) [Meter]

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Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

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Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs (FOR2730)

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Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)